1 MU-FASHION Multi-Resolution Data Fusion using Agent-Bearing Sensors In Hierarchically-Organized Networks Project Participants: Krishnendu Chakrabarty (Duke University) S. S. Iyengar (Louisiana State University Hairong Qi (University of Tennessee) DARPA SensIT PI Meeting Jan 17, 2002

2 Other Project Participants Vishnu Swaminathan (Duke University) Charles Schweizer (Duke University) Xiaoling Wang (University of Tennessee) Yuxin Tian (University of Tennessee, graduated) Yingyue Xu (University of Tennessee) Phani Teja Kuruganti (University of Tennessee) Qishi Wu (Louisiana State University) Lei Xu (Louisiana State University)

3 Project Goals and Components CSIP DistributedCentralized SP …… Base-line Signal Processing (node level) Local CSIP Global CSIP/ Decision Making Power/energy aware RTOS Sensor Deployment Algorithms Collaborative signal processing: energy aware, fault-tolerant, progressive accuracy Power management in real- time OS Fundamental research on sensor deployment

4 Accomplishments & National Recognition Fundamentals and new ideas Publications Experimentation and integration activities ONR Young Investigator Award (Chakrabarty) ACM Fellow (Iyengar)

5 Accomplishments (Fundamentals & New Ideas) Collaborative signal processing based on mobile agent paradigm Low-energy task scheduling for real-time operating systems (RTOS) Energy-driven I/O device scheduling algorithms Pruning-based optimal algorithm Analytical battery modeling Experimental validation of discharge and recovery Robust sensor deployment algorithms NP-Completeness proofs for sensor coverage problems Sensor deployment for a planar grid formulated as multidimensional combinatorial optimization problem. Maximize overall detection probability for given cost.

6 Accomplishments (Publications since April 2001) Conference papers: 4 published, 2 accepted, 1 submitted (under review) Journal papers: 3 published, 2 accepted, 2 submitted Guest editing of special issue of Journal of the Franklin Institute Guest editing of special issue of International Journal of High Performance Computing Applications – Special issue on Sensor Networks

7 Accomplishments (Integration and Experimentation Activities) Successfully deployed mobile agent for collaborative target classification Successful integration with BAEs low-level signal processing and Auburns distributed service for target classification and localization Could not integrate with PSU/ARL mobile code due to problems during compilation Attempted to port RTOS prototype to WINS 2.0 node Effort unsuccessful due to hardware difficulties, lack of technical support Successful in setting up a test bed based on the AMD Athlon-4 processor

8 Mobile-Agent-based Collaborative Signal Processing Integration code buffer itinerary ID Integration code buffer itinerary ID Integration code buffer itinerary ID Power-aware Progressive accuracy Small amount of data transfer Task adaptive

9 Amplitude stat. Local Target Classification Time series signal Power Spectral Density (PSD)Wavelet Analysis Shape stat. Peak selection Coefficients feature vectors (26 elements) Feature normalization, Principal Component Analysis (PCA) Target Classification (kNN)

10 Classification and Fusion Classification method: k-Nearest-Neighbors (kNN) Procedures of data fusion (At each node i, use kNN for each k {5,…,15}) Use the confidence ranges generated from each node as the overlapping function, apply multi-resolution integration (MRI) algorithm to get the fusion result confidence level confidence range smallestlargest in this column Class 1 Class 2 … Class n k=5 3/5 2/5 … 0 k=6 2/6 3/6 … 1/6 … … … … … k=15 10/15 4/15 … 1/15 {2/6, 10/15} {4/15, 3/6} … {0, 1/6}

11 Performance Gain Using Fusion Target close to A25 Target close to A01 Target close to A

12 November 2001 Demo Results Participate in the developmental demo Mobile-agent-based target classification is tested over Ethernet Mobile agents are deployed in four clusters with each cluster having four nodes Our training set has seismic data for AAV, DW, LAV, POV. During our time frame, available targets include AAV, LAV, DW, HMMVV Misclassify HMMVV as POV Correctly classify DW and AAV, LAV

13 Target Localization Use the energy measurements at each node and the energy decay model of signals to derive a circle indicating the possible position of a target

14 Illustration of Localization Node 1 (x1, y1, E1) Node 2 (x2, y2, E2) Mobile agent carries (x1, y1, E1) Node 3 (x3, y3, E3) (Cx1,Cy1,Cr1) derived from (x1,y1,E1) and (x2,y2,E2) Carry (x1,y1,E1), (x2,y2,E2), (Cx1,Cy1,Cr1) (Cx2,Cy2,Cr2) derived from (x1,y1,E1) and (x3,y3,E3) (Cx3,Cy3,Cr3) derived from (x2,y2,E2) and (x3,y3,E3) Target position (xi, yi): position of the node Ei: target energy sensed by node (Cxi, Cyi): center of the circle Cri: radius of the circle

15 Goals Computationally efficient and Power efficient Adaptability Progressive accuracy Real-time response Location-centric Each mobile agent is in charge of fusing data from sensors located in a certain area New features (Itinerary vs. Routing) Each node provides the same information with different accuracy Destination is unknown - every node is a potential destination Mobile-Agent-based Collaborative Signal Processing – Location Centric Itinerary

16 Ad Hoc Dynamic Itinerary Planning Local closest first (LCF) Faster in approaching the accuracy requirement Dash-line indicates the idea that the mobile agent does not have to migrate through all of the sensors in the cluster if it has achieved the accuracy requirement Spiral itinerary

17 Optimal Itinerary Design Other factors need to be considered Sensing quality (0 <= Hq <= 1) Hops needed from the current node (i) Leverage our dynamic power management research to handle constraint of remaining sensor power (0 <= Hp <= 1) Objective function Optimization problem – can be solved by genetic algorithm. Computation is done at the processing center.

18 RTOS-Driven Power Management Real-time system: application tasks have associated deadlines Sensor networks, nuclear power plants, avionics systems Power consumption directly influences availability, battery life, and number of field replacements Use of Dynamic Power Management (DPM) techniques greatly reduces power consumption

19 DPM Techniques Dynamic Power Management I/O-centricCPU-centric Real-time Non-RT Real-time Non-RT Our Research Focus Power reduction responsibility is transferred from hardware (BIOS) to software (OS) OS has global knowledge of CPU workload and devices (APM & ACPI) Power management through the operating system

20 CPU-centric DPM Previous work Low-Energy EDF scheduler (LEDF) Details presented in April 2001 PI Meeting Dynamically varies CPU voltage/frequency depending on workload (Dynamic Voltage Scaling) Guarantees that all task deadlines are met Implemented on RT-Linux test bed

21 Prototyping: Hardware Options Hitachi SH4 RTLinux port to SH4 still in its primitive stages No speed switching capability Full Power and Halt states Intel SpeedStep (High power mode and battery saver mode) Can control the state, but no control over specific frequency/voltage combinations. The hardware controls the voltage/frequency based on average load. AMD PowerNow! Can set voltage in 0.05V increments (each voltage has a corresponding MAX frequency). The 1.1 GHz Athlon processor uses a 1.4V core voltage. We can scale the voltage down to 1.25V with a frequency 700MHz. CPU power usage fV 2

22 Experimental Setup To outlet Multimeter AMD-Athlon Mobile CPU with PowerNow! capability, running RT- Linux v3.0 with LEDF 19V DC current Capacitor Capacitor used to smooth current Multimeter used to read current and voltage values Laptop runs with no battery and display turned off

23 Experimental Results: SensIT Task Sets Task # instns (millions) Exec. time (ms) Deadline (ms) Data acq Data cache Classification Processing Network Routing GUI update Housekeeping Speed (MHz)

24 Energy Savings Data SetDeadline Power consumed by EDF Power consumed by LEDF Energy savings Data set 1Tight33.85 W29.38 W13.2% Data set 2Moderate32.33 W27.08 W16.3% Data set 3Loose32.41 W22.31 W31.16%

25 I/O-centric DPM – EDS (new work since fall 2001) EDS (Energy-optimal Device Scheduler) generates energy-optimal device schedules Novel pruning based approach Energy-optimal solutions generated by re- ordering tasks and allowing flexible start times for the tasks Pruning becomes more effective as problem size increases

26 Example Jobj1j1 j2j2 j3j3 j4j4 j5j5 j6j6 j7j7 aiai cici didi Before reordering (non-optimal) j1j1 j3j3 j5j5 j7j7 j2j2 j4j4 j6j6 j6j6 1 2 k1k1 k2k2 After reordering (optimal) j1j1 j3j3 j5j5 j7j7 j2j2 j4j4 1 2 j6j6 k1k1 k2k2

27 Pruning Technique Jobj1j1 j2j2 j3j3 j4j4 j5j5 j6j6 j7j7 aiai cici didi Complete schedule tree Total # of vertices Total # of schedules E.EEDSE.EEDS

28 Experimental Results Total # of verticesTotal # of schedules E.EEDSSavingsE.EEDSSavings Job set H=20,J=9 H=30,J=11 H=35,J=12 H=40,J=13 H=45,J=14 H=55,J=16 H=60,J= % % % % 2.9x %1.6x % 23x %14x % DNF84107-DNF DNF DNF DNF DNF

29 High-level Battery Modeling (new direction since fall 2001) Develop high-level battery models for discharge and recovery Validate battery models on experimental test bed Alternating discharge and recovery prolongs battery life System lifetime is controlled by rate of switching Rate of switching is determined by discharge and recovery profiles of the batteries Discharge profile Empirical analytical model: V(t)=V 0 -V d (1-e - t ), t < LT Recovery profile Empirical analytical model: V(t)=V 0 +V r (1-e - t )

30 Experimental Setup Battery modelTypeCapacity (mAh) Threshold voltage SCH8500 (Samsung 8500) Li-ion H690H4 (Nokia 6100) Ni-MH Lamina R6PAlkalineData unavailable1.2 Battery type: SCH8500 Resistance: 18.5ohms Voltage output range: 3.60V to 4.15V Current output range: 195mA to 225mA Experiment parameters

31 Discharge Profile

32 Recovery Profile

33 Plans for Integrate with PSUs mobile code, test target localization, tracking For fixed sensor nodes, implement dynamic ad-hoc itinerary planning For mobile sensor nodes, dynamic itinerary planning on simulated wireless sensor networks. Performance evaluation between client/server integration paradigm and mobile-agent integration paradigm on the simulated network. Energy-driven RTOS design Implement and integrate energy-optimal I/O device scheduling Handle preemption and sporadic tasks, investigate eCOS as an implementation vehicle Adaptive re-prioritization based on available energy OS-driven battery scheduling Theoretical modeling, battery scheduling algorithms based on workload Effect of battery resistance on discharge & recovery Optimization framework based on coding theory for robust sensor deployment